Tree-Based Methods for Fuzzy Rule Extraction

Shuqing Zeng, Nan Zhang, and Juyang Weng, Michigan State University

This paper is concerned with the application of a tree-based regression model to extract fuzzy rules from high-dimensional data. We introduce a locally weighted scheme to the identification of Takagi-Sugeno type rules. It is proposed to apply the sequential least-squares method to estimate the linear model. A hierarchical clustering takes place in the product space of systems inputs and outputs and each path from the root to a leaf corresponds to a fuzzy IF-THEN rule. Only a subset of the rules is considered based on the locality of the input query data. At each hierarchy, a discriminating subspace is derived from the high-dimensional input space for a good generalization capability. Both a synthetic data set as well as a real-world robot navigation problem are considered to illustrate the working and the applicability of the algorithm


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.